Overview

Dataset statistics

Number of variables60
Number of observations202599
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory94.3 MiB
Average record size in memory488.0 B

Variable types

Categorical42
Numeric18

Warnings

image_id has a high cardinality: 202599 distinct values High cardinality
x_1_x is highly correlated with x_1_yHigh correlation
y_1_x is highly correlated with y_1_yHigh correlation
width_x is highly correlated with height_x and 2 other fieldsHigh correlation
height_x is highly correlated with width_x and 2 other fieldsHigh correlation
x_1_y is highly correlated with x_1_xHigh correlation
y_1_y is highly correlated with y_1_xHigh correlation
width_y is highly correlated with width_x and 2 other fieldsHigh correlation
height_y is highly correlated with width_x and 2 other fieldsHigh correlation
image_id is uniformly distributed Uniform
image_id has unique values Unique

Reproduction

Analysis started2021-02-20 22:43:52.738967
Analysis finished2021-02-20 22:46:54.814954
Duration3 minutes and 2.08 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

image_id
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct202599
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
155917.jpg
 
1
150787.jpg
 
1
176804.jpg
 
1
050662.jpg
 
1
001402.jpg
 
1
Other values (202594)
202594 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters2025990
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique202599 ?
Unique (%)100.0%

Sample

1st row000001.jpg
2nd row000002.jpg
3rd row000003.jpg
4th row000004.jpg
5th row000005.jpg
ValueCountFrequency (%)
155917.jpg1
 
< 0.1%
150787.jpg1
 
< 0.1%
176804.jpg1
 
< 0.1%
050662.jpg1
 
< 0.1%
001402.jpg1
 
< 0.1%
010667.jpg1
 
< 0.1%
003216.jpg1
 
< 0.1%
065677.jpg1
 
< 0.1%
028478.jpg1
 
< 0.1%
016766.jpg1
 
< 0.1%
Other values (202589)202589
> 99.9%
2021-02-20T23:46:55.410332image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
155917.jpg1
 
< 0.1%
150787.jpg1
 
< 0.1%
176804.jpg1
 
< 0.1%
050662.jpg1
 
< 0.1%
001402.jpg1
 
< 0.1%
010667.jpg1
 
< 0.1%
003216.jpg1
 
< 0.1%
065677.jpg1
 
< 0.1%
028478.jpg1
 
< 0.1%
016766.jpg1
 
< 0.1%
Other values (202589)202589
> 99.9%

Most occurring characters

ValueCountFrequency (%)
0204414
10.1%
.202599
10.0%
j202599
10.0%
p202599
10.0%
g202599
10.0%
1201820
10.0%
2104020
 
5.1%
3100820
 
5.0%
4100820
 
5.0%
5100820
 
5.0%
Other values (4)402880
19.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1215594
60.0%
Lowercase Letter607797
30.0%
Other Punctuation202599
 
10.0%

Most frequent character per category

ValueCountFrequency (%)
0204414
16.8%
1201820
16.6%
2104020
8.6%
3100820
8.3%
4100820
8.3%
5100820
8.3%
6100720
8.3%
7100720
8.3%
8100720
8.3%
9100720
8.3%
ValueCountFrequency (%)
j202599
33.3%
p202599
33.3%
g202599
33.3%
ValueCountFrequency (%)
.202599
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1418193
70.0%
Latin607797
30.0%

Most frequent character per script

ValueCountFrequency (%)
0204414
14.4%
.202599
14.3%
1201820
14.2%
2104020
7.3%
3100820
7.1%
4100820
7.1%
5100820
7.1%
6100720
7.1%
7100720
7.1%
8100720
7.1%
ValueCountFrequency (%)
j202599
33.3%
p202599
33.3%
g202599
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2025990
100.0%

Most frequent character per block

ValueCountFrequency (%)
0204414
10.1%
.202599
10.0%
j202599
10.0%
p202599
10.0%
g202599
10.0%
1201820
10.0%
2104020
 
5.1%
3100820
 
5.0%
4100820
 
5.0%
5100820
 
5.0%
Other values (4)402880
19.9%

5_o_Clock_Shadow
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
180083 
1
22516 

Length

Max length2
Median length2
Mean length1.88886421
Min length1

Characters and Unicode

Total characters382682
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1
ValueCountFrequency (%)
-1180083
88.9%
122516
 
11.1%
2021-02-20T23:46:55.639189image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:46:55.704561image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1202599
52.9%
-180083
47.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number202599
52.9%
Dash Punctuation180083
47.1%

Most frequent character per category

ValueCountFrequency (%)
-180083
100.0%
ValueCountFrequency (%)
1202599
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common382682
100.0%

Most frequent character per script

ValueCountFrequency (%)
1202599
52.9%
-180083
47.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII382682
100.0%

Most frequent character per block

ValueCountFrequency (%)
1202599
52.9%
-180083
47.1%

Arched_Eyebrows
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
148509 
1
54090 

Length

Max length2
Median length2
Mean length1.733019413
Min length1

Characters and Unicode

Total characters351108
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row-1
3rd row-1
4th row-1
5th row1
ValueCountFrequency (%)
-1148509
73.3%
154090
 
26.7%
2021-02-20T23:46:55.857541image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:46:55.921109image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1202599
57.7%
-148509
42.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number202599
57.7%
Dash Punctuation148509
42.3%

Most frequent character per category

ValueCountFrequency (%)
1202599
100.0%
ValueCountFrequency (%)
-148509
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common351108
100.0%

Most frequent character per script

ValueCountFrequency (%)
1202599
57.7%
-148509
42.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII351108
100.0%

Most frequent character per block

ValueCountFrequency (%)
1202599
57.7%
-148509
42.3%

Attractive
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
1
103833 
-1
98766 

Length

Max length2
Median length1
Mean length1.487495002
Min length1

Characters and Unicode

Total characters301365
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row-1
3rd row-1
4th row1
5th row1
ValueCountFrequency (%)
1103833
51.3%
-198766
48.7%
2021-02-20T23:46:56.079334image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:46:56.139855image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1202599
67.2%
-98766
32.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number202599
67.2%
Dash Punctuation98766
32.8%

Most frequent character per category

ValueCountFrequency (%)
1202599
100.0%
ValueCountFrequency (%)
-98766
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common301365
100.0%

Most frequent character per script

ValueCountFrequency (%)
1202599
67.2%
-98766
32.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII301365
100.0%

Most frequent character per block

ValueCountFrequency (%)
1202599
67.2%
-98766
32.8%

Bags_Under_Eyes
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
161153 
1
41446 

Length

Max length2
Median length2
Mean length1.795428408
Min length1

Characters and Unicode

Total characters363752
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row1
3rd row-1
4th row-1
5th row-1
ValueCountFrequency (%)
-1161153
79.5%
141446
 
20.5%
2021-02-20T23:46:56.302700image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:46:56.367494image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1202599
55.7%
-161153
44.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number202599
55.7%
Dash Punctuation161153
44.3%

Most frequent character per category

ValueCountFrequency (%)
-161153
100.0%
ValueCountFrequency (%)
1202599
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common363752
100.0%

Most frequent character per script

ValueCountFrequency (%)
1202599
55.7%
-161153
44.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII363752
100.0%

Most frequent character per block

ValueCountFrequency (%)
1202599
55.7%
-161153
44.3%

Bald
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
198052 
1
 
4547

Length

Max length2
Median length2
Mean length1.977556651
Min length1

Characters and Unicode

Total characters400651
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1
ValueCountFrequency (%)
-1198052
97.8%
14547
 
2.2%
2021-02-20T23:46:56.539193image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:46:56.604922image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1202599
50.6%
-198052
49.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number202599
50.6%
Dash Punctuation198052
49.4%

Most frequent character per category

ValueCountFrequency (%)
-198052
100.0%
ValueCountFrequency (%)
1202599
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common400651
100.0%

Most frequent character per script

ValueCountFrequency (%)
1202599
50.6%
-198052
49.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII400651
100.0%

Most frequent character per block

ValueCountFrequency (%)
1202599
50.6%
-198052
49.4%

Bangs
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
171890 
1
30709 

Length

Max length2
Median length2
Mean length1.848424721
Min length1

Characters and Unicode

Total characters374489
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1
ValueCountFrequency (%)
-1171890
84.8%
130709
 
15.2%
2021-02-20T23:46:56.772146image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:46:56.835504image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1202599
54.1%
-171890
45.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number202599
54.1%
Dash Punctuation171890
45.9%

Most frequent character per category

ValueCountFrequency (%)
-171890
100.0%
ValueCountFrequency (%)
1202599
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common374489
100.0%

Most frequent character per script

ValueCountFrequency (%)
1202599
54.1%
-171890
45.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII374489
100.0%

Most frequent character per block

ValueCountFrequency (%)
1202599
54.1%
-171890
45.9%

Big_Lips
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
153814 
1
48785 

Length

Max length2
Median length2
Mean length1.759204142
Min length1

Characters and Unicode

Total characters356413
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row1
4th row-1
5th row1
ValueCountFrequency (%)
-1153814
75.9%
148785
 
24.1%
2021-02-20T23:46:56.989719image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:46:57.054395image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1202599
56.8%
-153814
43.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number202599
56.8%
Dash Punctuation153814
43.2%

Most frequent character per category

ValueCountFrequency (%)
-153814
100.0%
ValueCountFrequency (%)
1202599
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common356413
100.0%

Most frequent character per script

ValueCountFrequency (%)
1202599
56.8%
-153814
43.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII356413
100.0%

Most frequent character per block

ValueCountFrequency (%)
1202599
56.8%
-153814
43.2%

Big_Nose
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
155083 
1
47516 

Length

Max length2
Median length2
Mean length1.765467747
Min length1

Characters and Unicode

Total characters357682
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row1
3rd row-1
4th row-1
5th row-1
ValueCountFrequency (%)
-1155083
76.5%
147516
 
23.5%
2021-02-20T23:46:57.215983image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:46:57.281881image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1202599
56.6%
-155083
43.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number202599
56.6%
Dash Punctuation155083
43.4%

Most frequent character per category

ValueCountFrequency (%)
-155083
100.0%
ValueCountFrequency (%)
1202599
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common357682
100.0%

Most frequent character per script

ValueCountFrequency (%)
1202599
56.6%
-155083
43.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII357682
100.0%

Most frequent character per block

ValueCountFrequency (%)
1202599
56.6%
-155083
43.4%

Black_Hair
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
154127 
1
48472 

Length

Max length2
Median length2
Mean length1.760749066
Min length1

Characters and Unicode

Total characters356726
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1
ValueCountFrequency (%)
-1154127
76.1%
148472
 
23.9%
2021-02-20T23:46:57.556138image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:46:57.622755image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1202599
56.8%
-154127
43.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number202599
56.8%
Dash Punctuation154127
43.2%

Most frequent character per category

ValueCountFrequency (%)
-154127
100.0%
ValueCountFrequency (%)
1202599
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common356726
100.0%

Most frequent character per script

ValueCountFrequency (%)
1202599
56.8%
-154127
43.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII356726
100.0%

Most frequent character per block

ValueCountFrequency (%)
1202599
56.8%
-154127
43.2%

Blond_Hair
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
172616 
1
29983 

Length

Max length2
Median length2
Mean length1.852008154
Min length1

Characters and Unicode

Total characters375215
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1
ValueCountFrequency (%)
-1172616
85.2%
129983
 
14.8%
2021-02-20T23:46:57.784453image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:46:57.848979image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1202599
54.0%
-172616
46.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number202599
54.0%
Dash Punctuation172616
46.0%

Most frequent character per category

ValueCountFrequency (%)
-172616
100.0%
ValueCountFrequency (%)
1202599
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common375215
100.0%

Most frequent character per script

ValueCountFrequency (%)
1202599
54.0%
-172616
46.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII375215
100.0%

Most frequent character per block

ValueCountFrequency (%)
1202599
54.0%
-172616
46.0%

Blurry
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
192287 
1
 
10312

Length

Max length2
Median length2
Mean length1.949101427
Min length1

Characters and Unicode

Total characters394886
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row1
4th row-1
5th row-1
ValueCountFrequency (%)
-1192287
94.9%
110312
 
5.1%
2021-02-20T23:46:58.018193image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:46:58.083515image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1202599
51.3%
-192287
48.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number202599
51.3%
Dash Punctuation192287
48.7%

Most frequent character per category

ValueCountFrequency (%)
-192287
100.0%
ValueCountFrequency (%)
1202599
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common394886
100.0%

Most frequent character per script

ValueCountFrequency (%)
1202599
51.3%
-192287
48.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII394886
100.0%

Most frequent character per block

ValueCountFrequency (%)
1202599
51.3%
-192287
48.7%

Brown_Hair
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
161027 
1
41572 

Length

Max length2
Median length2
Mean length1.79480649
Min length1

Characters and Unicode

Total characters363626
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row-1
4th row-1
5th row-1
ValueCountFrequency (%)
-1161027
79.5%
141572
 
20.5%
2021-02-20T23:46:58.246408image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:46:58.311770image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1202599
55.7%
-161027
44.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number202599
55.7%
Dash Punctuation161027
44.3%

Most frequent character per category

ValueCountFrequency (%)
1202599
100.0%
ValueCountFrequency (%)
-161027
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common363626
100.0%

Most frequent character per script

ValueCountFrequency (%)
1202599
55.7%
-161027
44.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII363626
100.0%

Most frequent character per block

ValueCountFrequency (%)
1202599
55.7%
-161027
44.3%

Bushy_Eyebrows
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
173796 
1
28803 

Length

Max length2
Median length2
Mean length1.857832467
Min length1

Characters and Unicode

Total characters376395
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1
ValueCountFrequency (%)
-1173796
85.8%
128803
 
14.2%
2021-02-20T23:46:58.506290image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:46:58.571912image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1202599
53.8%
-173796
46.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number202599
53.8%
Dash Punctuation173796
46.2%

Most frequent character per category

ValueCountFrequency (%)
-173796
100.0%
ValueCountFrequency (%)
1202599
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common376395
100.0%

Most frequent character per script

ValueCountFrequency (%)
1202599
53.8%
-173796
46.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII376395
100.0%

Most frequent character per block

ValueCountFrequency (%)
1202599
53.8%
-173796
46.2%

Chubby
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
190936 
1
 
11663

Length

Max length2
Median length2
Mean length1.942433082
Min length1

Characters and Unicode

Total characters393535
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1
ValueCountFrequency (%)
-1190936
94.2%
111663
 
5.8%
2021-02-20T23:46:58.736646image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:46:58.820075image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1202599
51.5%
-190936
48.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number202599
51.5%
Dash Punctuation190936
48.5%

Most frequent character per category

ValueCountFrequency (%)
-190936
100.0%
ValueCountFrequency (%)
1202599
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common393535
100.0%

Most frequent character per script

ValueCountFrequency (%)
1202599
51.5%
-190936
48.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII393535
100.0%

Most frequent character per block

ValueCountFrequency (%)
1202599
51.5%
-190936
48.5%

Double_Chin
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
193140 
1
 
9459

Length

Max length2
Median length2
Mean length1.953311714
Min length1

Characters and Unicode

Total characters395739
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1
ValueCountFrequency (%)
-1193140
95.3%
19459
 
4.7%
2021-02-20T23:46:59.010474image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:46:59.088568image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1202599
51.2%
-193140
48.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number202599
51.2%
Dash Punctuation193140
48.8%

Most frequent character per category

ValueCountFrequency (%)
-193140
100.0%
ValueCountFrequency (%)
1202599
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common395739
100.0%

Most frequent character per script

ValueCountFrequency (%)
1202599
51.2%
-193140
48.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII395739
100.0%

Most frequent character per block

ValueCountFrequency (%)
1202599
51.2%
-193140
48.8%

Eyeglasses
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
189406 
1
 
13193

Length

Max length2
Median length2
Mean length1.934881219
Min length1

Characters and Unicode

Total characters392005
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1
ValueCountFrequency (%)
-1189406
93.5%
113193
 
6.5%
2021-02-20T23:46:59.289440image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:46:59.360154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1202599
51.7%
-189406
48.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number202599
51.7%
Dash Punctuation189406
48.3%

Most frequent character per category

ValueCountFrequency (%)
-189406
100.0%
ValueCountFrequency (%)
1202599
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common392005
100.0%

Most frequent character per script

ValueCountFrequency (%)
1202599
51.7%
-189406
48.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII392005
100.0%

Most frequent character per block

ValueCountFrequency (%)
1202599
51.7%
-189406
48.3%

Goatee
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
189883 
1
 
12716

Length

Max length2
Median length2
Mean length1.937235623
Min length1

Characters and Unicode

Total characters392482
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1
ValueCountFrequency (%)
-1189883
93.7%
112716
 
6.3%
2021-02-20T23:46:59.523215image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:46:59.586355image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1202599
51.6%
-189883
48.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number202599
51.6%
Dash Punctuation189883
48.4%

Most frequent character per category

ValueCountFrequency (%)
-189883
100.0%
ValueCountFrequency (%)
1202599
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common392482
100.0%

Most frequent character per script

ValueCountFrequency (%)
1202599
51.6%
-189883
48.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII392482
100.0%

Most frequent character per block

ValueCountFrequency (%)
1202599
51.6%
-189883
48.4%

Gray_Hair
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
194100 
1
 
8499

Length

Max length2
Median length2
Mean length1.958050138
Min length1

Characters and Unicode

Total characters396699
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1
ValueCountFrequency (%)
-1194100
95.8%
18499
 
4.2%
2021-02-20T23:46:59.765604image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:46:59.947377image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1202599
51.1%
-194100
48.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number202599
51.1%
Dash Punctuation194100
48.9%

Most frequent character per category

ValueCountFrequency (%)
-194100
100.0%
ValueCountFrequency (%)
1202599
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common396699
100.0%

Most frequent character per script

ValueCountFrequency (%)
1202599
51.1%
-194100
48.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII396699
100.0%

Most frequent character per block

ValueCountFrequency (%)
1202599
51.1%
-194100
48.9%

Heavy_Makeup
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
124209 
1
78390 

Length

Max length2
Median length2
Mean length1.613078051
Min length1

Characters and Unicode

Total characters326808
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row-1
3rd row-1
4th row-1
5th row1
ValueCountFrequency (%)
-1124209
61.3%
178390
38.7%
2021-02-20T23:47:00.099213image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:47:00.161644image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1202599
62.0%
-124209
38.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number202599
62.0%
Dash Punctuation124209
38.0%

Most frequent character per category

ValueCountFrequency (%)
1202599
100.0%
ValueCountFrequency (%)
-124209
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common326808
100.0%

Most frequent character per script

ValueCountFrequency (%)
1202599
62.0%
-124209
38.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII326808
100.0%

Most frequent character per block

ValueCountFrequency (%)
1202599
62.0%
-124209
38.0%

High_Cheekbones
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
110410 
1
92189 

Length

Max length2
Median length2
Mean length1.544968139
Min length1

Characters and Unicode

Total characters313009
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row-1
4th row-1
5th row-1
ValueCountFrequency (%)
-1110410
54.5%
192189
45.5%
2021-02-20T23:47:00.328306image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:47:00.389379image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1202599
64.7%
-110410
35.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number202599
64.7%
Dash Punctuation110410
35.3%

Most frequent character per category

ValueCountFrequency (%)
1202599
100.0%
ValueCountFrequency (%)
-110410
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common313009
100.0%

Most frequent character per script

ValueCountFrequency (%)
1202599
64.7%
-110410
35.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII313009
100.0%

Most frequent character per block

ValueCountFrequency (%)
1202599
64.7%
-110410
35.3%

Male
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
118165 
1
84434 

Length

Max length2
Median length2
Mean length1.583245722
Min length1

Characters and Unicode

Total characters320764
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row1
4th row-1
5th row-1
ValueCountFrequency (%)
-1118165
58.3%
184434
41.7%
2021-02-20T23:47:00.554394image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:47:00.618168image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1202599
63.2%
-118165
36.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number202599
63.2%
Dash Punctuation118165
36.8%

Most frequent character per category

ValueCountFrequency (%)
-118165
100.0%
ValueCountFrequency (%)
1202599
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common320764
100.0%

Most frequent character per script

ValueCountFrequency (%)
1202599
63.2%
-118165
36.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII320764
100.0%

Most frequent character per block

ValueCountFrequency (%)
1202599
63.2%
-118165
36.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
104657 
1
97942 

Length

Max length2
Median length2
Mean length1.516572145
Min length1

Characters and Unicode

Total characters307256
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row-1
4th row-1
5th row-1
ValueCountFrequency (%)
-1104657
51.7%
197942
48.3%
2021-02-20T23:47:00.786669image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:47:00.847231image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1202599
65.9%
-104657
34.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number202599
65.9%
Dash Punctuation104657
34.1%

Most frequent character per category

ValueCountFrequency (%)
1202599
100.0%
ValueCountFrequency (%)
-104657
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common307256
100.0%

Most frequent character per script

ValueCountFrequency (%)
1202599
65.9%
-104657
34.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII307256
100.0%

Most frequent character per block

ValueCountFrequency (%)
1202599
65.9%
-104657
34.1%

Mustache
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
194182 
1
 
8417

Length

Max length2
Median length2
Mean length1.958454879
Min length1

Characters and Unicode

Total characters396781
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1
ValueCountFrequency (%)
-1194182
95.8%
18417
 
4.2%
2021-02-20T23:47:01.020486image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:47:01.090343image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1202599
51.1%
-194182
48.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number202599
51.1%
Dash Punctuation194182
48.9%

Most frequent character per category

ValueCountFrequency (%)
-194182
100.0%
ValueCountFrequency (%)
1202599
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common396781
100.0%

Most frequent character per script

ValueCountFrequency (%)
1202599
51.1%
-194182
48.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII396781
100.0%

Most frequent character per block

ValueCountFrequency (%)
1202599
51.1%
-194182
48.9%

Narrow_Eyes
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
179270 
1
23329 

Length

Max length2
Median length2
Mean length1.884851357
Min length1

Characters and Unicode

Total characters381869
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row1
4th row-1
5th row1
ValueCountFrequency (%)
-1179270
88.5%
123329
 
11.5%
2021-02-20T23:47:01.268767image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:47:01.332936image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1202599
53.1%
-179270
46.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number202599
53.1%
Dash Punctuation179270
46.9%

Most frequent character per category

ValueCountFrequency (%)
-179270
100.0%
ValueCountFrequency (%)
1202599
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common381869
100.0%

Most frequent character per script

ValueCountFrequency (%)
1202599
53.1%
-179270
46.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII381869
100.0%

Most frequent character per block

ValueCountFrequency (%)
1202599
53.1%
-179270
46.9%

No_Beard
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
1
169158 
-1
33441 

Length

Max length2
Median length1
Mean length1.165060045
Min length1

Characters and Unicode

Total characters236040
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
1169158
83.5%
-133441
 
16.5%
2021-02-20T23:47:01.496769image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:47:01.562781image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1202599
85.8%
-33441
 
14.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number202599
85.8%
Dash Punctuation33441
 
14.2%

Most frequent character per category

ValueCountFrequency (%)
1202599
100.0%
ValueCountFrequency (%)
-33441
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common236040
100.0%

Most frequent character per script

ValueCountFrequency (%)
1202599
85.8%
-33441
 
14.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII236040
100.0%

Most frequent character per block

ValueCountFrequency (%)
1202599
85.8%
-33441
 
14.2%

Oval_Face
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
145032 
1
57567 

Length

Max length2
Median length2
Mean length1.715857433
Min length1

Characters and Unicode

Total characters347631
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1
ValueCountFrequency (%)
-1145032
71.6%
157567
 
28.4%
2021-02-20T23:47:01.720662image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:47:01.785553image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1202599
58.3%
-145032
41.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number202599
58.3%
Dash Punctuation145032
41.7%

Most frequent character per category

ValueCountFrequency (%)
-145032
100.0%
ValueCountFrequency (%)
1202599
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common347631
100.0%

Most frequent character per script

ValueCountFrequency (%)
1202599
58.3%
-145032
41.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII347631
100.0%

Most frequent character per block

ValueCountFrequency (%)
1202599
58.3%
-145032
41.7%

Pale_Skin
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
193898 
1
 
8701

Length

Max length2
Median length2
Mean length1.957053095
Min length1

Characters and Unicode

Total characters396497
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1
ValueCountFrequency (%)
-1193898
95.7%
18701
 
4.3%
2021-02-20T23:47:01.975137image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:47:02.050673image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1202599
51.1%
-193898
48.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number202599
51.1%
Dash Punctuation193898
48.9%

Most frequent character per category

ValueCountFrequency (%)
-193898
100.0%
ValueCountFrequency (%)
1202599
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common396497
100.0%

Most frequent character per script

ValueCountFrequency (%)
1202599
51.1%
-193898
48.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII396497
100.0%

Most frequent character per block

ValueCountFrequency (%)
1202599
51.1%
-193898
48.9%

Pointy_Nose
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
146389 
1
56210 

Length

Max length2
Median length2
Mean length1.722555393
Min length1

Characters and Unicode

Total characters348988
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row-1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
-1146389
72.3%
156210
 
27.7%
2021-02-20T23:47:02.342847image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:47:02.407466image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1202599
58.1%
-146389
41.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number202599
58.1%
Dash Punctuation146389
41.9%

Most frequent character per category

ValueCountFrequency (%)
1202599
100.0%
ValueCountFrequency (%)
-146389
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common348988
100.0%

Most frequent character per script

ValueCountFrequency (%)
1202599
58.1%
-146389
41.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII348988
100.0%

Most frequent character per block

ValueCountFrequency (%)
1202599
58.1%
-146389
41.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
186436 
1
 
16163

Length

Max length2
Median length2
Mean length1.920221719
Min length1

Characters and Unicode

Total characters389035
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1
ValueCountFrequency (%)
-1186436
92.0%
116163
 
8.0%
2021-02-20T23:47:02.573560image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:47:02.639854image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1202599
52.1%
-186436
47.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number202599
52.1%
Dash Punctuation186436
47.9%

Most frequent character per category

ValueCountFrequency (%)
-186436
100.0%
ValueCountFrequency (%)
1202599
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common389035
100.0%

Most frequent character per script

ValueCountFrequency (%)
1202599
52.1%
-186436
47.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII389035
100.0%

Most frequent character per block

ValueCountFrequency (%)
1202599
52.1%
-186436
47.9%

Rosy_Cheeks
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
189284 
1
 
13315

Length

Max length2
Median length2
Mean length1.934279044
Min length1

Characters and Unicode

Total characters391883
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1
ValueCountFrequency (%)
-1189284
93.4%
113315
 
6.6%
2021-02-20T23:47:02.809164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:47:02.877621image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1202599
51.7%
-189284
48.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number202599
51.7%
Dash Punctuation189284
48.3%

Most frequent character per category

ValueCountFrequency (%)
-189284
100.0%
ValueCountFrequency (%)
1202599
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common391883
100.0%

Most frequent character per script

ValueCountFrequency (%)
1202599
51.7%
-189284
48.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII391883
100.0%

Most frequent character per block

ValueCountFrequency (%)
1202599
51.7%
-189284
48.3%

Sideburns
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
191150 
1
 
11449

Length

Max length2
Median length2
Mean length1.943489356
Min length1

Characters and Unicode

Total characters393749
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1
ValueCountFrequency (%)
-1191150
94.3%
111449
 
5.7%
2021-02-20T23:47:03.065661image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:47:03.133575image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1202599
51.5%
-191150
48.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number202599
51.5%
Dash Punctuation191150
48.5%

Most frequent character per category

ValueCountFrequency (%)
-191150
100.0%
ValueCountFrequency (%)
1202599
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common393749
100.0%

Most frequent character per script

ValueCountFrequency (%)
1202599
51.5%
-191150
48.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII393749
100.0%

Most frequent character per block

ValueCountFrequency (%)
1202599
51.5%
-191150
48.5%

Smiling
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
104930 
1
97669 

Length

Max length2
Median length2
Mean length1.517919634
Min length1

Characters and Unicode

Total characters307529
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row-1
4th row-1
5th row-1
ValueCountFrequency (%)
-1104930
51.8%
197669
48.2%
2021-02-20T23:47:03.321670image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:47:03.389367image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1202599
65.9%
-104930
34.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number202599
65.9%
Dash Punctuation104930
34.1%

Most frequent character per category

ValueCountFrequency (%)
1202599
100.0%
ValueCountFrequency (%)
-104930
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common307529
100.0%

Most frequent character per script

ValueCountFrequency (%)
1202599
65.9%
-104930
34.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII307529
100.0%

Most frequent character per block

ValueCountFrequency (%)
1202599
65.9%
-104930
34.1%

Straight_Hair
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
160377 
1
42222 

Length

Max length2
Median length2
Mean length1.791598182
Min length1

Characters and Unicode

Total characters362976
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row-1
3rd row-1
4th row1
5th row-1
ValueCountFrequency (%)
-1160377
79.2%
142222
 
20.8%
2021-02-20T23:47:03.568788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:47:03.641317image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1202599
55.8%
-160377
44.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number202599
55.8%
Dash Punctuation160377
44.2%

Most frequent character per category

ValueCountFrequency (%)
1202599
100.0%
ValueCountFrequency (%)
-160377
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common362976
100.0%

Most frequent character per script

ValueCountFrequency (%)
1202599
55.8%
-160377
44.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII362976
100.0%

Most frequent character per block

ValueCountFrequency (%)
1202599
55.8%
-160377
44.2%

Wavy_Hair
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
137855 
1
64744 

Length

Max length2
Median length2
Mean length1.680432776
Min length1

Characters and Unicode

Total characters340454
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row1
4th row-1
5th row-1
ValueCountFrequency (%)
-1137855
68.0%
164744
32.0%
2021-02-20T23:47:03.817508image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:47:03.889920image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1202599
59.5%
-137855
40.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number202599
59.5%
Dash Punctuation137855
40.5%

Most frequent character per category

ValueCountFrequency (%)
-137855
100.0%
ValueCountFrequency (%)
1202599
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common340454
100.0%

Most frequent character per script

ValueCountFrequency (%)
1202599
59.5%
-137855
40.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII340454
100.0%

Most frequent character per block

ValueCountFrequency (%)
1202599
59.5%
-137855
40.5%

Wearing_Earrings
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
164323 
1
38276 

Length

Max length2
Median length2
Mean length1.811075079
Min length1

Characters and Unicode

Total characters366922
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row-1
3rd row-1
4th row1
5th row-1
ValueCountFrequency (%)
-1164323
81.1%
138276
 
18.9%
2021-02-20T23:47:04.075738image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:47:04.144456image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1202599
55.2%
-164323
44.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number202599
55.2%
Dash Punctuation164323
44.8%

Most frequent character per category

ValueCountFrequency (%)
1202599
100.0%
ValueCountFrequency (%)
-164323
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common366922
100.0%

Most frequent character per script

ValueCountFrequency (%)
1202599
55.2%
-164323
44.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII366922
100.0%

Most frequent character per block

ValueCountFrequency (%)
1202599
55.2%
-164323
44.8%

Wearing_Hat
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
192781 
1
 
9818

Length

Max length2
Median length2
Mean length1.951539741
Min length1

Characters and Unicode

Total characters395380
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1
ValueCountFrequency (%)
-1192781
95.2%
19818
 
4.8%
2021-02-20T23:47:04.316305image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:47:04.383170image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1202599
51.2%
-192781
48.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number202599
51.2%
Dash Punctuation192781
48.8%

Most frequent character per category

ValueCountFrequency (%)
-192781
100.0%
ValueCountFrequency (%)
1202599
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common395380
100.0%

Most frequent character per script

ValueCountFrequency (%)
1202599
51.2%
-192781
48.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII395380
100.0%

Most frequent character per block

ValueCountFrequency (%)
1202599
51.2%
-192781
48.8%

Wearing_Lipstick
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
106884 
1
95715 

Length

Max length2
Median length2
Mean length1.527564302
Min length1

Characters and Unicode

Total characters309483
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row-1
3rd row-1
4th row1
5th row1
ValueCountFrequency (%)
-1106884
52.8%
195715
47.2%
2021-02-20T23:47:04.539873image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:47:04.601286image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1202599
65.5%
-106884
34.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number202599
65.5%
Dash Punctuation106884
34.5%

Most frequent character per category

ValueCountFrequency (%)
1202599
100.0%
ValueCountFrequency (%)
-106884
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common309483
100.0%

Most frequent character per script

ValueCountFrequency (%)
1202599
65.5%
-106884
34.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII309483
100.0%

Most frequent character per block

ValueCountFrequency (%)
1202599
65.5%
-106884
34.5%

Wearing_Necklace
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
177686 
1
24913 

Length

Max length2
Median length2
Mean length1.877032957
Min length1

Characters and Unicode

Total characters380285
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row1
5th row-1
ValueCountFrequency (%)
-1177686
87.7%
124913
 
12.3%
2021-02-20T23:47:04.889983image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:47:04.953133image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1202599
53.3%
-177686
46.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number202599
53.3%
Dash Punctuation177686
46.7%

Most frequent character per category

ValueCountFrequency (%)
-177686
100.0%
ValueCountFrequency (%)
1202599
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common380285
100.0%

Most frequent character per script

ValueCountFrequency (%)
1202599
53.3%
-177686
46.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII380285
100.0%

Most frequent character per block

ValueCountFrequency (%)
1202599
53.3%
-177686
46.7%

Wearing_Necktie
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
187867 
1
 
14732

Length

Max length2
Median length2
Mean length1.927284932
Min length1

Characters and Unicode

Total characters390466
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1
ValueCountFrequency (%)
-1187867
92.7%
114732
 
7.3%
2021-02-20T23:47:05.115124image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:47:05.178372image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1202599
51.9%
-187867
48.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number202599
51.9%
Dash Punctuation187867
48.1%

Most frequent character per category

ValueCountFrequency (%)
-187867
100.0%
ValueCountFrequency (%)
1202599
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common390466
100.0%

Most frequent character per script

ValueCountFrequency (%)
1202599
51.9%
-187867
48.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII390466
100.0%

Most frequent character per block

ValueCountFrequency (%)
1202599
51.9%
-187867
48.1%

Young
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
1
156734 
-1
45865 

Length

Max length2
Median length1
Mean length1.226383151
Min length1

Characters and Unicode

Total characters248464
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
1156734
77.4%
-145865
 
22.6%
2021-02-20T23:47:05.337692image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:47:05.403884image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1202599
81.5%
-45865
 
18.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number202599
81.5%
Dash Punctuation45865
 
18.5%

Most frequent character per category

ValueCountFrequency (%)
1202599
100.0%
ValueCountFrequency (%)
-45865
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common248464
100.0%

Most frequent character per script

ValueCountFrequency (%)
1202599
81.5%
-45865
 
18.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII248464
100.0%

Most frequent character per block

ValueCountFrequency (%)
1202599
81.5%
-45865
 
18.5%

x_1_x
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1550
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156.7645645
Minimum1
Maximum3840
Zeros0
Zeros (%)0.0%
Memory size3.1 MiB
2021-02-20T23:47:05.480789image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile25
Q169
median110
Q3181
95-th percentile461
Maximum3840
Range3839
Interquartile range (IQR)112

Descriptive statistics

Standard deviation164.518135
Coefficient of variation (CV)1.049459969
Kurtosis28.12359739
Mean156.7645645
Median Absolute Deviation (MAD)50
Skewness3.930000137
Sum31760344
Variance27066.21673
MonotocityNot monotonic
2021-02-20T23:47:05.604148image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
831333
 
0.7%
901330
 
0.7%
801328
 
0.7%
791322
 
0.7%
921299
 
0.6%
861298
 
0.6%
811297
 
0.6%
951296
 
0.6%
851294
 
0.6%
731274
 
0.6%
Other values (1540)189528
93.5%
ValueCountFrequency (%)
11178
0.6%
2131
 
0.1%
3188
 
0.1%
4182
 
0.1%
5213
 
0.1%
ValueCountFrequency (%)
38401
< 0.1%
38151
< 0.1%
36881
< 0.1%
36011
< 0.1%
35381
< 0.1%

y_1_x
Real number (ℝ≥0)

HIGH CORRELATION

Distinct901
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.33550511
Minimum0
Maximum1858
Zeros1
Zeros (%)< 0.1%
Memory size3.1 MiB
2021-02-20T23:47:05.734613image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20
Q144
median68
Q398
95-th percentile204
Maximum1858
Range1858
Interquartile range (IQR)54

Descriptive statistics

Standard deviation76.06728441
Coefficient of variation (CV)0.9019603821
Kurtosis35.80350661
Mean84.33550511
Median Absolute Deviation (MAD)26
Skewness4.48863673
Sum17086289
Variance5786.231758
MonotocityNot monotonic
2021-02-20T23:47:05.854414image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
492262
 
1.1%
502252
 
1.1%
602216
 
1.1%
572203
 
1.1%
512198
 
1.1%
552196
 
1.1%
462184
 
1.1%
582183
 
1.1%
532179
 
1.1%
442176
 
1.1%
Other values (891)180550
89.1%
ValueCountFrequency (%)
01
 
< 0.1%
11876
0.9%
2134
 
0.1%
3174
 
0.1%
4199
 
0.1%
ValueCountFrequency (%)
18581
< 0.1%
17241
< 0.1%
17101
< 0.1%
16901
< 0.1%
15551
< 0.1%

width_x
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1016
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean194.754061
Minimum0
Maximum3827
Zeros1
Zeros (%)< 0.1%
Memory size3.1 MiB
2021-02-20T23:47:05.984753image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile75
Q1120
median164
Q3221
95-th percentile419
Maximum3827
Range3827
Interquartile range (IQR)101

Descriptive statistics

Standard deviation141.7700661
Coefficient of variation (CV)0.727944082
Kurtosis31.54555994
Mean194.754061
Median Absolute Deviation (MAD)50
Skewness4.300759707
Sum39456978
Variance20098.75165
MonotocityNot monotonic
2021-02-20T23:47:06.109182image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1333140
 
1.5%
1403063
 
1.5%
1253018
 
1.5%
1563015
 
1.5%
1433010
 
1.5%
1303001
 
1.5%
1382998
 
1.5%
1462975
 
1.5%
1352958
 
1.5%
1272947
 
1.5%
Other values (1006)172474
85.1%
ValueCountFrequency (%)
01
 
< 0.1%
33
< 0.1%
53
< 0.1%
84
< 0.1%
107
< 0.1%
ValueCountFrequency (%)
38271
< 0.1%
30761
< 0.1%
26032
< 0.1%
25322
< 0.1%
25091
< 0.1%

height_x
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1249
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean268.9223293
Minimum0
Maximum5299
Zeros1
Zeros (%)< 0.1%
Memory size3.1 MiB
2021-02-20T23:47:06.236395image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile104
Q1166
median227
Q3306
95-th percentile576
Maximum5299
Range5299
Interquartile range (IQR)140

Descriptive statistics

Standard deviation195.6649357
Coefficient of variation (CV)0.727589026
Kurtosis31.7086195
Mean268.9223293
Median Absolute Deviation (MAD)68
Skewness4.31885815
Sum54483395
Variance38284.76704
MonotocityNot monotonic
2021-02-20T23:47:06.361370image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1843090
 
1.5%
1943034
 
1.5%
2162963
 
1.5%
1912962
 
1.5%
1732961
 
1.5%
1982950
 
1.5%
1802947
 
1.5%
1872931
 
1.4%
2022931
 
1.4%
2052913
 
1.4%
Other values (1239)172917
85.3%
ValueCountFrequency (%)
01
 
< 0.1%
43
< 0.1%
73
< 0.1%
114
< 0.1%
147
< 0.1%
ValueCountFrequency (%)
52991
< 0.1%
42591
< 0.1%
36042
< 0.1%
35062
< 0.1%
34421
< 0.1%

x_1_y
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1550
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156.7645645
Minimum1
Maximum3840
Zeros0
Zeros (%)0.0%
Memory size3.1 MiB
2021-02-20T23:47:06.486682image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile25
Q169
median110
Q3181
95-th percentile461
Maximum3840
Range3839
Interquartile range (IQR)112

Descriptive statistics

Standard deviation164.518135
Coefficient of variation (CV)1.049459969
Kurtosis28.12359739
Mean156.7645645
Median Absolute Deviation (MAD)50
Skewness3.930000137
Sum31760344
Variance27066.21673
MonotocityNot monotonic
2021-02-20T23:47:06.752376image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
831333
 
0.7%
901330
 
0.7%
801328
 
0.7%
791322
 
0.7%
921299
 
0.6%
861298
 
0.6%
811297
 
0.6%
951296
 
0.6%
851294
 
0.6%
731274
 
0.6%
Other values (1540)189528
93.5%
ValueCountFrequency (%)
11178
0.6%
2131
 
0.1%
3188
 
0.1%
4182
 
0.1%
5213
 
0.1%
ValueCountFrequency (%)
38401
< 0.1%
38151
< 0.1%
36881
< 0.1%
36011
< 0.1%
35381
< 0.1%

y_1_y
Real number (ℝ≥0)

HIGH CORRELATION

Distinct901
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.33550511
Minimum0
Maximum1858
Zeros1
Zeros (%)< 0.1%
Memory size3.1 MiB
2021-02-20T23:47:06.895169image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20
Q144
median68
Q398
95-th percentile204
Maximum1858
Range1858
Interquartile range (IQR)54

Descriptive statistics

Standard deviation76.06728441
Coefficient of variation (CV)0.9019603821
Kurtosis35.80350661
Mean84.33550511
Median Absolute Deviation (MAD)26
Skewness4.48863673
Sum17086289
Variance5786.231758
MonotocityNot monotonic
2021-02-20T23:47:07.022103image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
492262
 
1.1%
502252
 
1.1%
602216
 
1.1%
572203
 
1.1%
512198
 
1.1%
552196
 
1.1%
462184
 
1.1%
582183
 
1.1%
532179
 
1.1%
442176
 
1.1%
Other values (891)180550
89.1%
ValueCountFrequency (%)
01
 
< 0.1%
11876
0.9%
2134
 
0.1%
3174
 
0.1%
4199
 
0.1%
ValueCountFrequency (%)
18581
< 0.1%
17241
< 0.1%
17101
< 0.1%
16901
< 0.1%
15551
< 0.1%

width_y
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1016
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean194.754061
Minimum0
Maximum3827
Zeros1
Zeros (%)< 0.1%
Memory size3.1 MiB
2021-02-20T23:47:07.162328image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile75
Q1120
median164
Q3221
95-th percentile419
Maximum3827
Range3827
Interquartile range (IQR)101

Descriptive statistics

Standard deviation141.7700661
Coefficient of variation (CV)0.727944082
Kurtosis31.54555994
Mean194.754061
Median Absolute Deviation (MAD)50
Skewness4.300759707
Sum39456978
Variance20098.75165
MonotocityNot monotonic
2021-02-20T23:47:07.296741image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1333140
 
1.5%
1403063
 
1.5%
1253018
 
1.5%
1563015
 
1.5%
1433010
 
1.5%
1303001
 
1.5%
1382998
 
1.5%
1462975
 
1.5%
1352958
 
1.5%
1272947
 
1.5%
Other values (1006)172474
85.1%
ValueCountFrequency (%)
01
 
< 0.1%
33
< 0.1%
53
< 0.1%
84
< 0.1%
107
< 0.1%
ValueCountFrequency (%)
38271
< 0.1%
30761
< 0.1%
26032
< 0.1%
25322
< 0.1%
25091
< 0.1%

height_y
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1249
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean268.9223293
Minimum0
Maximum5299
Zeros1
Zeros (%)< 0.1%
Memory size3.1 MiB
2021-02-20T23:47:07.442698image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile104
Q1166
median227
Q3306
95-th percentile576
Maximum5299
Range5299
Interquartile range (IQR)140

Descriptive statistics

Standard deviation195.6649357
Coefficient of variation (CV)0.727589026
Kurtosis31.7086195
Mean268.9223293
Median Absolute Deviation (MAD)68
Skewness4.31885815
Sum54483395
Variance38284.76704
MonotocityNot monotonic
2021-02-20T23:47:07.570023image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1843090
 
1.5%
1943034
 
1.5%
2162963
 
1.5%
1912962
 
1.5%
1732961
 
1.5%
1982950
 
1.5%
1802947
 
1.5%
1872931
 
1.4%
2022931
 
1.4%
2052913
 
1.4%
Other values (1239)172917
85.3%
ValueCountFrequency (%)
01
 
< 0.1%
43
< 0.1%
73
< 0.1%
114
< 0.1%
147
< 0.1%
ValueCountFrequency (%)
52991
< 0.1%
42591
< 0.1%
36042
< 0.1%
35062
< 0.1%
34421
< 0.1%

partition
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
0
162770 
2
19962 
1
19867 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters202599
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0162770
80.3%
219962
 
9.9%
119867
 
9.8%
2021-02-20T23:47:07.798550image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-20T23:47:07.863999image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0162770
80.3%
219962
 
9.9%
119867
 
9.8%

Most occurring characters

ValueCountFrequency (%)
0162770
80.3%
219962
 
9.9%
119867
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number202599
100.0%

Most frequent character per category

ValueCountFrequency (%)
0162770
80.3%
219962
 
9.9%
119867
 
9.8%

Most occurring scripts

ValueCountFrequency (%)
Common202599
100.0%

Most frequent character per script

ValueCountFrequency (%)
0162770
80.3%
219962
 
9.9%
119867
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII202599
100.0%

Most frequent character per block

ValueCountFrequency (%)
0162770
80.3%
219962
 
9.9%
119867
 
9.8%

lefteye_x
Real number (ℝ≥0)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.3538665
Minimum56
Maximum88
Zeros0
Zeros (%)0.0%
Memory size3.1 MiB
2021-02-20T23:47:07.937680image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum56
5-th percentile67
Q168
median69
Q370
95-th percentile72
Maximum88
Range32
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.717951717
Coefficient of variation (CV)0.02477081385
Kurtosis7.926731219
Mean69.3538665
Median Absolute Deviation (MAD)1
Skewness1.557458496
Sum14051024
Variance2.951358102
MonotocityNot monotonic
2021-02-20T23:47:08.035219image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
6957572
28.4%
7046522
23.0%
6841033
20.3%
7122194
 
11.0%
6716132
 
8.0%
728278
 
4.1%
733308
 
1.6%
662991
 
1.5%
741479
 
0.7%
75842
 
0.4%
Other values (21)2248
 
1.1%
ValueCountFrequency (%)
563
 
< 0.1%
591
 
< 0.1%
607
< 0.1%
615
 
< 0.1%
6215
< 0.1%
ValueCountFrequency (%)
881
 
< 0.1%
872
 
< 0.1%
8610
 
< 0.1%
8513
 
< 0.1%
8433
< 0.1%

lefteye_y
Real number (ℝ≥0)

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111.1979822
Minimum98
Maximum129
Zeros0
Zeros (%)0.0%
Memory size3.1 MiB
2021-02-20T23:47:08.139501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum98
5-th percentile109
Q1111
median111
Q3112
95-th percentile113
Maximum129
Range31
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.129284177
Coefficient of variation (CV)0.01015561753
Kurtosis6.733065491
Mean111.1979822
Median Absolute Deviation (MAD)1
Skewness-0.9105428779
Sum22528600
Variance1.275282753
MonotocityNot monotonic
2021-02-20T23:47:08.232954image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
11186618
42.8%
11263290
31.2%
11027396
 
13.5%
11311992
 
5.9%
1096502
 
3.2%
1082255
 
1.1%
1142030
 
1.0%
107968
 
0.5%
106457
 
0.2%
115418
 
0.2%
Other values (17)673
 
0.3%
ValueCountFrequency (%)
981
 
< 0.1%
994
 
< 0.1%
1003
 
< 0.1%
1018
 
< 0.1%
10222
< 0.1%
ValueCountFrequency (%)
1291
 
< 0.1%
1281
 
< 0.1%
1241
 
< 0.1%
1213
< 0.1%
1204
< 0.1%

righteye_x
Real number (ℝ≥0)

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.6440308
Minimum90
Maximum124
Zeros0
Zeros (%)0.0%
Memory size3.1 MiB
2021-02-20T23:47:08.335681image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile105
Q1107
median108
Q3109
95-th percentile110
Maximum124
Range34
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.690252194
Coefficient of variation (CV)0.01570223803
Kurtosis8.032769637
Mean107.6440308
Median Absolute Deviation (MAD)1
Skewness-1.534524977
Sum21808573
Variance2.856952481
MonotocityNot monotonic
2021-02-20T23:47:08.437095image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
10858536
28.9%
10747429
23.4%
10940771
20.1%
10622202
 
11.0%
11015252
 
7.5%
1058057
 
4.0%
1043069
 
1.5%
1112860
 
1.4%
1031384
 
0.7%
102792
 
0.4%
Other values (22)2247
 
1.1%
ValueCountFrequency (%)
903
 
< 0.1%
9110
 
< 0.1%
9217
< 0.1%
9318
< 0.1%
9437
< 0.1%
ValueCountFrequency (%)
1241
 
< 0.1%
1221
 
< 0.1%
1211
 
< 0.1%
1201
 
< 0.1%
1177
< 0.1%

righteye_y
Real number (ℝ≥0)

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111.1616
Minimum95
Maximum122
Zeros0
Zeros (%)0.0%
Memory size3.1 MiB
2021-02-20T23:47:08.667254image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum95
5-th percentile109
Q1111
median111
Q3112
95-th percentile113
Maximum122
Range27
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.16922944
Coefficient of variation (CV)0.01051828545
Kurtosis6.284448824
Mean111.1616
Median Absolute Deviation (MAD)1
Skewness-1.103770291
Sum22521229
Variance1.367097482
MonotocityNot monotonic
2021-02-20T23:47:08.771555image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
11183850
41.4%
11262759
31.0%
11028686
 
14.2%
11312568
 
6.2%
1097553
 
3.7%
1082736
 
1.4%
1141727
 
0.9%
1071093
 
0.5%
106541
 
0.3%
115349
 
0.2%
Other values (15)737
 
0.4%
ValueCountFrequency (%)
951
 
< 0.1%
994
 
< 0.1%
1005
 
< 0.1%
10112
 
< 0.1%
10231
< 0.1%
ValueCountFrequency (%)
1222
 
< 0.1%
1213
 
< 0.1%
1204
 
< 0.1%
11913
< 0.1%
11817
< 0.1%

nose_x
Real number (ℝ≥0)

Distinct65
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.0631395
Minimum57
Maximum121
Zeros0
Zeros (%)0.0%
Memory size3.1 MiB
2021-02-20T23:47:08.885676image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum57
5-th percentile77
Q184
median88
Q392
95-th percentile99
Maximum121
Range64
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.647732917
Coefficient of variation (CV)0.07548825711
Kurtosis0.7875224343
Mean88.0631395
Median Absolute Deviation (MAD)4
Skewness0.04770735828
Sum17841504
Variance44.19235293
MonotocityNot monotonic
2021-02-20T23:47:09.001253image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8817621
 
8.7%
8917027
 
8.4%
8715319
 
7.6%
9014090
 
7.0%
8612280
 
6.1%
9111358
 
5.6%
8510033
 
5.0%
928992
 
4.4%
848455
 
4.2%
837420
 
3.7%
Other values (55)80004
39.5%
ValueCountFrequency (%)
571
 
< 0.1%
582
 
< 0.1%
592
 
< 0.1%
601
 
< 0.1%
619
< 0.1%
ValueCountFrequency (%)
1212
 
< 0.1%
1201
 
< 0.1%
1193
 
< 0.1%
1181
 
< 0.1%
11710
< 0.1%

nose_y
Real number (ℝ≥0)

Distinct55
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean135.1020242
Minimum93
Maximum156
Zeros0
Zeros (%)0.0%
Memory size3.1 MiB
2021-02-20T23:47:09.123424image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum93
5-th percentile128
Q1133
median135
Q3138
95-th percentile141
Maximum156
Range63
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.245077982
Coefficient of variation (CV)0.03142127594
Kurtosis1.410874684
Mean135.1020242
Median Absolute Deviation (MAD)3
Skewness-0.6504310632
Sum27371535
Variance18.02068707
MonotocityNot monotonic
2021-02-20T23:47:09.239963image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13621254
10.5%
13720433
10.1%
13520237
10.0%
13418143
9.0%
13817765
8.8%
13315554
 
7.7%
13914319
 
7.1%
13212480
 
6.2%
14010577
 
5.2%
1319469
 
4.7%
Other values (45)42368
20.9%
ValueCountFrequency (%)
931
< 0.1%
1002
< 0.1%
1022
< 0.1%
1041
< 0.1%
1052
< 0.1%
ValueCountFrequency (%)
1561
 
< 0.1%
1541
 
< 0.1%
1532
 
< 0.1%
1522
 
< 0.1%
1515
< 0.1%

leftmouth_x
Real number (ℝ≥0)

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.24745927
Minimum57
Maximum90
Zeros0
Zeros (%)0.0%
Memory size3.1 MiB
2021-02-20T23:47:09.358981image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum57
5-th percentile66
Q169
median72
Q373
95-th percentile76
Maximum90
Range33
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.168010688
Coefficient of variation (CV)0.04446489349
Kurtosis0.07061811884
Mean71.24745927
Median Absolute Deviation (MAD)2
Skewness-0.06342102468
Sum14434664
Variance10.03629172
MonotocityNot monotonic
2021-02-20T23:47:09.460852image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
7326372
13.0%
7225824
12.7%
7122193
11.0%
7422062
10.9%
7019044
9.4%
6916556
8.2%
6814792
7.3%
7513991
6.9%
6712178
6.0%
668318
 
4.1%
Other values (24)21269
10.5%
ValueCountFrequency (%)
573
 
< 0.1%
585
 
< 0.1%
592
 
< 0.1%
6019
 
< 0.1%
6156
< 0.1%
ValueCountFrequency (%)
901
 
< 0.1%
892
 
< 0.1%
889
 
< 0.1%
8714
< 0.1%
8634
< 0.1%

leftmouth_y
Real number (ℝ≥0)

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean152.1130114
Minimum116
Maximum174
Zeros0
Zeros (%)0.0%
Memory size3.1 MiB
2021-02-20T23:47:09.571558image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum116
5-th percentile150
Q1151
median152
Q3153
95-th percentile155
Maximum174
Range58
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.799343093
Coefficient of variation (CV)0.0118289887
Kurtosis6.929804665
Mean152.1130114
Median Absolute Deviation (MAD)1
Skewness1.225924984
Sum30817944
Variance3.237635567
MonotocityNot monotonic
2021-02-20T23:47:09.681477image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
15254751
27.0%
15143767
21.6%
15340326
19.9%
15022155
10.9%
15418801
 
9.3%
1557360
 
3.6%
1496583
 
3.2%
1563195
 
1.6%
1571494
 
0.7%
1481392
 
0.7%
Other values (23)2775
 
1.4%
ValueCountFrequency (%)
1161
 
< 0.1%
1402
 
< 0.1%
1411
 
< 0.1%
1422
 
< 0.1%
1435
< 0.1%
ValueCountFrequency (%)
1741
 
< 0.1%
1701
 
< 0.1%
1693
 
< 0.1%
1689
 
< 0.1%
16734
< 0.1%

rightmouth_x
Real number (ℝ≥0)

Distinct37
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.5864294
Minimum82
Maximum120
Zeros0
Zeros (%)0.0%
Memory size3.1 MiB
2021-02-20T23:47:09.801032image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum82
5-th percentile101
Q1103
median105
Q3108
95-th percentile111
Maximum120
Range38
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.233124793
Coefficient of variation (CV)0.03062064711
Kurtosis0.08226024505
Mean105.5864294
Median Absolute Deviation (MAD)2
Skewness0.03374175801
Sum21391705
Variance10.45309593
MonotocityNot monotonic
2021-02-20T23:47:09.911755image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
10426081
12.9%
10524589
12.1%
10322891
11.3%
10621178
10.5%
10718250
9.0%
10816187
8.0%
10215596
7.7%
10914319
7.1%
11011918
5.9%
1018396
 
4.1%
Other values (27)23194
11.4%
ValueCountFrequency (%)
821
 
< 0.1%
831
 
< 0.1%
851
 
< 0.1%
862
< 0.1%
883
< 0.1%
ValueCountFrequency (%)
1202
 
< 0.1%
1191
 
< 0.1%
1184
 
< 0.1%
11711
 
< 0.1%
11640
< 0.1%

rightmouth_y
Real number (ℝ≥0)

Distinct36
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean152.1946604
Minimum114
Maximum173
Zeros0
Zeros (%)0.0%
Memory size3.1 MiB
2021-02-20T23:47:10.035819image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum114
5-th percentile150
Q1151
median152
Q3153
95-th percentile155
Maximum173
Range59
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.752368479
Coefficient of variation (CV)0.01151399448
Kurtosis7.234418468
Mean152.1946604
Median Absolute Deviation (MAD)1
Skewness1.136074568
Sum30834486
Variance3.070795287
MonotocityNot monotonic
2021-02-20T23:47:10.132177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
15256327
27.8%
15343140
21.3%
15142294
20.9%
15419900
 
9.8%
15019155
 
9.5%
1557930
 
3.9%
1495425
 
2.7%
1563162
 
1.6%
1571497
 
0.7%
1481230
 
0.6%
Other values (26)2539
 
1.3%
ValueCountFrequency (%)
1141
< 0.1%
1381
< 0.1%
1392
< 0.1%
1401
< 0.1%
1411
< 0.1%
ValueCountFrequency (%)
1731
 
< 0.1%
1721
 
< 0.1%
1701
 
< 0.1%
1697
< 0.1%
1682
 
< 0.1%

Interactions

2021-02-20T23:46:04.141465image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:04.293617image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:04.442106image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:04.583426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:04.731126image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:04.881945image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:05.023721image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:05.161681image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:05.300081image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:05.438687image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:05.580046image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:05.718810image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:05.856136image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:05.997627image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:06.134486image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:06.273313image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:06.414178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:06.549774image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:06.692811image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:06.831614image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:07.076141image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:07.219674image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:07.371302image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:07.523452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:07.654022image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:07.788283image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:07.927099image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:08.062065image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:08.194596image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:08.330166image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:08.476920image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:08.619735image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:08.764171image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:08.937056image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:09.073322image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:09.220444image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:09.365233image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:09.507806image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:09.667941image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:09.813705image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:09.971458image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:10.109798image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:10.250414image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:10.390514image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:10.533389image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:10.674488image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:10.926405image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:11.065305image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:11.204266image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:11.340494image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:11.479555image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:11.614470image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:11.746024image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:11.872599image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:12.003942image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:12.134578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:12.261915image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:12.391858image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:12.515677image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:12.641507image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:12.768629image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:12.897609image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:13.022022image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:13.149009image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:13.279379image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:13.422057image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:13.555585image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:13.713416image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:13.868293image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:14.022781image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:14.166149image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:14.308571image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:14.444653image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:14.709293image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:14.853351image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:14.990312image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:15.143287image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:15.282789image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:15.422200image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:15.557464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:15.696434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:15.833290image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:15.969299image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:16.103481image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:16.243506image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:16.378443image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:16.517694image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:16.651968image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:16.788780image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:16.921271image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:17.058236image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:17.197365image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:17.332822image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:17.464267image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:17.596036image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:17.735644image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:17.878684image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:18.036473image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:18.174201image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:18.419171image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:18.549993image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:18.685277image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:18.815525image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:18.956070image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:19.097650image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:19.240917image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:19.374809image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:19.518035image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:19.684445image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:19.836089image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-20T23:46:19.980674image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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Correlations

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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-20T23:47:11.909708image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-20T23:47:13.120449image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-20T23:47:14.265016image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-02-20T23:47:15.389722image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-02-20T23:46:48.743555image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-20T23:46:52.752403image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

image_id5_o_Clock_ShadowArched_EyebrowsAttractiveBags_Under_EyesBaldBangsBig_LipsBig_NoseBlack_HairBlond_HairBlurryBrown_HairBushy_EyebrowsChubbyDouble_ChinEyeglassesGoateeGray_HairHeavy_MakeupHigh_CheekbonesMaleMouth_Slightly_OpenMustacheNarrow_EyesNo_BeardOval_FacePale_SkinPointy_NoseReceding_HairlineRosy_CheeksSideburnsSmilingStraight_HairWavy_HairWearing_EarringsWearing_HatWearing_LipstickWearing_NecklaceWearing_NecktieYoungx_1_xy_1_xwidth_xheight_xx_1_yy_1_ywidth_yheight_ypartitionlefteye_xlefteye_yrighteye_xrighteye_ynose_xnose_yleftmouth_xleftmouth_yrightmouth_xrightmouth_y
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1000002.jpg-1-1-11-1-1-11-1-1-11-1-1-1-1-1-1-11-11-1-11-1-1-1-1-1-11-1-1-1-1-1-1-11729422130672942213060691101071128113570151108153
2000003.jpg-1-1-1-1-1-11-1-1-11-1-1-1-1-1-1-1-1-11-1-111-1-11-1-1-1-1-11-1-1-1-1-11216599112621659911260761121041061081287415698158
3000004.jpg-1-11-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-11-1-11-1-1-1-11-11-111-1162225756478162225756478107211310810810113871155101151
4000005.jpg-111-1-1-11-1-1-1-1-1-1-1-1-1-1-11-1-1-1-111-1-11-1-1-1-1-1-1-1-11-1-112361091201662361091201660661141121128611971147104150
5000006.jpg-111-1-1-11-1-1-1-11-1-1-1-1-1-11-1-11-1-11-1-1-1-1-1-1-1-111-11-1-1114667182252146671822520711111061109413174154102153
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7000008.jpg11-11-1-11-11-1-1-1-1-1-1-1-1-1-1-11-1-1-11-1-11-1-1-1-1-1-1-1-1-1-1-1121289218302212892183020711101061118413773155104153
8000009.jpg-111-1-111-1-1-1-1-1-1-1-1-1-1-111-11-1-111-11-11-11-1-11-11-1-116002743434756002743434750681131101119713966152109150
9000010.jpg-1-11-1-1-1-1-1-1-1-1-1-1-1-1-1-1-111-1-1-1-11-1-1-1-1-1-1-1-11-1-11-1-111131102112921131102112920681111081128913670151107151

Last rows

image_id5_o_Clock_ShadowArched_EyebrowsAttractiveBags_Under_EyesBaldBangsBig_LipsBig_NoseBlack_HairBlond_HairBlurryBrown_HairBushy_EyebrowsChubbyDouble_ChinEyeglassesGoateeGray_HairHeavy_MakeupHigh_CheekbonesMaleMouth_Slightly_OpenMustacheNarrow_EyesNo_BeardOval_FacePale_SkinPointy_NoseReceding_HairlineRosy_CheeksSideburnsSmilingStraight_HairWavy_HairWearing_EarringsWearing_HatWearing_LipstickWearing_NecklaceWearing_NecktieYoungx_1_xy_1_xwidth_xheight_xx_1_yy_1_ywidth_yheight_ypartitionlefteye_xlefteye_yrighteye_xrighteye_ynose_xnose_yleftmouth_xleftmouth_yrightmouth_xrightmouth_y
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202592202593.jpg-1-11-1-11-1-1-1-1-1-1-1-1-1-1-1-111-11-1111-11-1-1-11-1-1-1-11-1-1192103190263921031902632681121091128614169151107152
202593202594.jpg-111-1-1-11-1-1-1-1-11-1-1-1-1-11-1-1-1-1-11-1-11-1-1-1-1-111-11-1-111081272443381081272443382691111081119113974153103151
202594202595.jpg-1-11-1-1-11-1-11-1-1-1-1-1-1-1-1-1-1-1-1-1-11-1-1-1-1-1-1-1-1-1-1-11-1-111381912213061381912213062691111081118914073151104153
202595202596.jpg-1-1-1-1-111-1-11-1-1-1-1-1-1-1-1-1111-111-11-1-1-1-111-1-1-1-1-1-111371291141581371291141582671121101128514166150110150
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